2023/08/28 by Christopher Lee, Lee, Christopher
Medicine · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astro and Planetary Science #Astrobiology #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Geography #Geology #Ground truth #Hogan #Impact crater #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Mars Exploration Program #Physics #Planetary Science and Exploration #Remote sensing #Spaceflight effects on biology
paper · pdf · doi:10.48550/arxiv.2308.14650
openalex publication_date 2023/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Crater mapping using neural networks and other automated methods has increased recently with automated Crater Detection Algorithms (CDAs) applied to planetary bodies throughout the solar system. A recent publication by Benedix et al. (2020) showed high performance at small scales compared to similar automated CDAs but with a net positive diameter bias in many crater candidates. I compare the publicly available catalogs from Benedix et al. (2020) and Lee & Hogan (2021) and show that the reported performance is sensitive to the metrics used to test the catalogs. I show how the more permissive comparison methods indicate a higher CDA performance by allowing worse candidate craters to match ground-truth craters. I show that the Benedix et al. (2020) catalog has a substantial performance loss with increasing latitude and identify an image projection issue that might cause this loss. Finally, I suggest future applications of neural networks in generating large scientific datasets be validated using secondary networks with independent data sources or training methods.